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Found 49 papers, 24 papers with code
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task
Progress on NLP for mental health — indeed, for healthcare in general — is hampered by obstacles to shared, community-level access to relevant data.
TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users
To complement this evaluation, we propose a dynamic thresholding technique that adjusts the classifier’s sensitivity as a function of the number of posts a user has.
A Reproducibility Study of PLAID
The PLAID (Performance-optimized Late Interaction Driver) algorithm for ColBERTv2 uses clustered term representations to retrieve and progressively prune documents for final (exact) document scoring.
Overview of the TREC 2023 NeuCLIR Track
The principal tasks are ranked retrieval of news in one of the three languages, using English topics.
Shallow Cross-Encoders for Low-Latency Retrieval
However, Cross-Encoders based on large transformer models (such as BERT or T5) are computationally expensive and allow for scoring only a small number of documents within a reasonably small latency window.
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
We introduce our dataset FollowIR, which contains a rigorous instruction evaluation benchmark as well as a training set for helping IR models learn to better follow real-world instructions.
Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models
Modern sequence-to-sequence relevance models like monoT5 can effectively capture complex textual interactions between queries and documents through cross-encoding.
Evaluating the Explainability of Neural Rankers
While the increasing complexity of the search models have been able to demonstrate improvements in effectiveness (measured in terms of relevance of top-retrieved results), a question worthy of a thorough inspection is - "how explainable are these models?
A Deep Learning Approach for Selective Relevance Feedback
Additionally, to further improve retrieval effectiveness with this selective PRF approach, we make use of the model's confidence estimates to combine the information from the original and expanded queries.
Generative Query Reformulation for Effective Adhoc Search
GenQR directly reformulates the user's input query, while GenPRF provides additional context for the query by making use of pseudo-relevance feedback information.
On the Effects of Regional Spelling Conventions in Retrieval Models
One advantage of neural ranking models is that they are meant to generalise well in situations of synonymity i. e. where two words have similar or identical meanings.
Lexically-Accelerated Dense Retrieval
We introduce 'LADR' (Lexically-Accelerated Dense Retrieval), a simple-yet-effective approach that improves the efficiency of existing dense retrieval models without compromising on retrieval effectiveness.
Adaptive Latent Entity Expansion for Document Retrieval
First, we demonstrate that applying a strong neural re-ranker before sparse or dense PRF can improve the retrieval effectiveness by 5-8%.
Online Distillation for Pseudo-Relevance Feedback
Model distillation has emerged as a prominent technique to improve neural search models.
The Information Retrieval Experiment Platform
Standardization is achieved when a retrieval approach implements PyTerrier's interfaces and the input and output of an experiment are compatible with ir_datasets and ir_measures.
Adapting Learned Sparse Retrieval for Long Documents
We investigate existing aggregation approaches for adapting LSR to longer documents and find that proximal scoring is crucial for LSR to handle long documents.
Overview of the TREC 2022 NeuCLIR Track
This is the first year of the TREC Neural CLIR (NeuCLIR) track, which aims to study the impact of neural approaches to cross-language information retrieval.
A Unified Framework for Learned Sparse Retrieval
We then reproduce all prominent methods using a common codebase and re-train them in the same environment, which allows us to quantify how components of the framework affect effectiveness and efficiency.
One-Shot Labeling for Automatic Relevance Estimation
We then explore various approaches for predicting the relevance of unjudged documents with respect to a query and the known relevant document, including nearest neighbor, supervised, and prompting techniques.
Doc2Query--: When Less is More
Doc2Query -- the process of expanding the content of a document before indexing using a sequence-to-sequence model -- has emerged as a prominent technique for improving the first-stage retrieval effectiveness of search engines.
Adaptive Re-Ranking with a Corpus Graph
Search systems often employ a re-ranking pipeline, wherein documents (or passages) from an initial pool of candidates are assigned new ranking scores.
CODEC: Complex Document and Entity Collection
We also show that the manual query reformulations significantly improve document ranking and entity ranking performance.
On Survivorship Bias in MS MARCO
We observe that this bias could be present in the popular MS MARCO dataset, given that annotators could not find answers to 38--45% of the queries, leading to these queries being discarded in training and evaluation processes.
Reproducing Personalised Session Search over the AOL Query Log
Given that web documents are prone to change over time, we study the differences present between a version of the corpus containing documents as they appeared in 2017 (which has been used by several recent works) and a new version we construct that includes documents close to as they appeared at the time the query log was produced (2006).
Streamlining Evaluation with ir-measures
We present ir-measures, a new tool that makes it convenient to calculate a diverse set of evaluation measures used in information retrieval.
Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations
We show that such a selection strategy often results in higher cumulative regret and to this end, we propose a selection strategy based on the maximum utility of the arms.
IntenT5: Search Result Diversification using Causal Language Models
Existing approaches often rely on massive query logs and interaction data to generate a variety of possible query intents, which then can be used to re-rank documents.
Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review
We indeed find that the pre-trained BERT model reduces review cost by 10% to 15% in TAR workflows simulated on the RCV1-v2 newswire collection.
Simplified Data Wrangling with ir_datasets
Managing the data for Information Retrieval (IR) experiments can be challenging.
ToxCCIn: Toxic Content Classification with Interpretability
Despite the recent successes of transformer-based models in terms of effectiveness on a variety of tasks, their decisions often remain opaque to humans.
ABNIRML: Analyzing the Behavior of Neural IR Models
Pretrained contextualized language models such as BERT and T5 have established a new state-of-the-art for ad-hoc search.
SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search
With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus.
PARADE: Passage Representation Aggregation for Document Reranking
In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score.
Ranked #2 on
Ad-Hoc Information Retrieval
on TREC Robust04
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
Offensive language detection is an important and challenging task in natural language processing.
Interaction Matching for Long-Tail Multi-Label Classification
We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be useful for ad-hoc search result ranking.
SLEDGE: A Simple Yet Effective Baseline for COVID-19 Scientific Knowledge Search
In this work, we present a search system called SLEDGE, which utilizes SciBERT to effectively re-rank articles.
Efficient Document Re-Ranking for Transformers by Precomputing Term Representations
Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking.
Training Curricula for Open Domain Answer Re-Ranking
We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process.
Expansion via Prediction of Importance with Contextualization
We also observe that the performance is additive with the current leading first-stage retrieval methods, further narrowing the gap between inexpensive and cost-prohibitive passage ranking approaches.
Ranking Significant Discrepancies in Clinical Reports
This allows medical practitioners to easily identify and learn from the reports in which their interpretation most substantially differed from that of the attending physician (who finalized the report).
Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning
While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages.
Ontology-Aware Clinical Abstractive Summarization
Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors.
CEDR: Contextualized Embeddings for Document Ranking
We call this joint approach CEDR (Contextualized Embeddings for Document Ranking).
Ranked #3 on
Ad-Hoc Information Retrieval
on TREC Robust04
RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media.
SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions
Mental health is a significant and growing public health concern.
A Deeper Look into Dependency-Based Word Embeddings
We investigate the effect of various dependency-based word embeddings on distinguishing between functional and domain similarity, word similarity rankings, and two downstream tasks in English.
GU IRLAB at SemEval-2018 Task 7: Tree-LSTMs for Scientific Relation Classification
SemEval 2018 Task 7 focuses on relation ex- traction and classification in scientific literature.
GUIR at SemEval-2017 Task 12: A Framework for Cross-Domain Clinical Temporal Information Extraction
Clinical TempEval 2017 (SemEval 2017 Task 12) addresses the task of cross-domain temporal extraction from clinical text.
Content-Based Weak Supervision for Ad-Hoc Re-Ranking
One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training.
